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massive farming data over long-time scales. These long-term farming data brings more opportunities on leveraging emerging machine learning methods to examine the effects of multiple factors on crop growth
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. This PhD project will build directly on this work by using ideas from machine learning—originally developed to study the movement of larger organisms—to understand how bacteria process information in
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Automatization and Digital Enhancement of Characterisation Techniques: Joining the Dots between AI, Machine Learning and Materials Advances
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unit and individual and collaborative research in the area of Power Electronic, Machine and Control. The role holder will be expected to conduct and lead high-caliber, impactful research at the forefront
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Generalisable, Physics-Aware Machine Learning for Gas Turbine Engines
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The Opportunity: We invite applications for a Postdoctoral Research Associate in machine learning based in the School of Informatics, University of Edinburgh. The postholder will also be formally
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cardiovascular MRI, cardiac diffusion tensor imaging, and machine learning or deep learning methods applied to imaging is highly desirable. You will be a collaborative and motivated researcher, capable of working
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Artificial intelligence and machine learning methods for model discovery in the social sciences
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/DPhil in robotics, computer science, machine learning, informatics, AI, or a closely related field. You will have an excellent academic track record in topics relevant to locomotion and manipulation; path
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from both generalists and specialists including those who self-identify as statisticians, software engineers, machine learning practitioners, data scientists or data engineers. CANDIDATE PROFILE Please